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This paper aims to explore the meaning of smart office environments from a user perspective by investigating user preferences and expectations.

Eleven semi-structured interviews with the users after moving into a smart office building of a Dutch Municipality and an observation as complementary data were conducted. The data were analysed based on the grounded theory and thematic analysis, combining a reflexive approach to the literature review.

Two main themes were revealed addressing user expectations and preferences for smart office environments: “enhanced interaction” with the social and physical office environment and “sense-making” of the smart concept (or smartness). Within these themes, basic and smart office aspects were identified and classified based on their association with smart office concepts or technology.

The findings reveal the meaning of the smart office concepts from a user perspective by highlighting the importance of user experience on enhanced interaction and sense-making of the smart office concept, equipped with basic and smart aspects.

To the best of the authors’ knowledge, this study is the first to qualitatively examine drivers underlying the meaning of smart office concepts from a user point of view. Organisations, environmental psychologists, designers and managers can use the findings of this study to develop guidelines for a successful smart office design.

The coronavirus disease 2019 (COVID-19) pandemic has had a big impact on organisations globally, leaving organisations with no choice but to adapt to the new reality of remote work to ensure business continuity. Such an unexpected reality created the conditions for testing new applications of smart home technology whilst working from home. Given the potential implications of such applications to improve the working environment, and a lack of research on that front, this paper pursued two objectives. First, the paper explored the impact of smart home applications by examining the factors that could contribute to perceived productivity and well-being whilst working from home. Second, the study investigated the role of productivity and well-being in motivating the intention of remote workers to use smart home technologies in a home-work environment in the future.

The study adopted a cross-sectional research design. For data collection, 528 smart home users working from home during the pandemic were recruited. Collected data were analysed using a structural equation modelling approach.

The results of the research confirmed that perceived productivity is dependent on service relevance, perceived usefulness, innovativeness, hedonic beliefs and control over environmental conditions. Perceived well-being correlates with task-technology fit, service relevance, perceived usefulness, perceived ease of use, attitude to smart homes, innovativeness, hedonic beliefs and control over environmental conditions. Intention to work from a smart home-office in the future is dependent on perceived well-being.

The findings of the research contribute to the organisational and smart home literature, by providing missing evidence about the implications of the application of smart home technologies for employees' perceived productivity and well-being. The paper considers the conditions that facilitate better outcomes during remote work and could potentially be used to improve the work environment in offices after the pandemic. Also, the findings inform smart home developers about the features of technology which could improve the developers' application in contexts beyond home settings.

Pervasive computing enhances the environment by embedding many computers that are gracefully integrated with human users. The purpose of this paper is to describe the creation of a smart context‐aware environment in which computation follows people and serves them everywhere. Building such smart environments is still difficult and complex due to lacking a uniform infrastructure that can adapt to diverse smart domains.

To address this problem, the paper proposes an agent‐based pluggable infrastructure which integrates a mobile agent system named pvMogent, establishes an ontology‐based context model and introduces a workflow‐based application model with the open services gateway initiative (OSGi) framework. By plugging corresponding domain context in ontology model and different applications, the infrastructure can be customized to various domains.

Through the implementation of several context‐aware applications, it was found that the infrastructure can largely reduce the development complexity as well as keep the domain extensibility by plugging corresponding domain context in ontology model.

In this paper, a number of key techniques are explored which are suitable for building context‐awareness. The experiences and lessons learned from the system development could further facilitate and inspire the research in this direction.

This study aims to explore an emerging collection of smart building technologies, known as smart workplace solutions (SWS), in the context of facilities management (FM).

This study is based on semi-structured interviews with facility managers in Finland, Norway and Sweden who have deployed SWSs in their organizations. SWS features, based on empirical data from a previous study, were also used to further analyse the interviews.

It analyses the benefits that SWSs bring from the facility management point of view. It is clear that the impetus for change and for deploying SWS in the context of FM is primarily driven by cost savings related to reductions in office space.

This research has been conducted with a focus on office buildings only. However, other building types can learn from the benefits that facility managers receive in the area of user-centred smart buildings.

SWSs are often seen as employee experience solutions that are only related to “soft” elements such as collaboration, innovation and learning. Understanding the FM business case can help make a more practical case for their deployment.

SWSs are an emerging area, and this study has collected data from facility managers who use them daily.

The purpose of this paper is to study heterogeneous transfer learning for activity recognition using heuristic search techniques. Many pervasive computing applications require information about the activities currently being performed, but activity recognition algorithms typically require substantial amounts of labeled training data for each setting. One solution to this problem is to leverage transfer learning techniques to reuse available labeled data in new situations.

This paper introduces three novel heterogeneous transfer learning techniques that reverse the typical transfer model and map the target feature space to the source feature space and apply them to activity recognition in a smart apartment. This paper evaluates the techniques on data from 18 different smart apartments located in an assisted-care facility and compares the results against several baselines.

The three transfer learning techniques are all able to outperform the baseline comparisons in several situations. Furthermore, the techniques are successfully used in an ensemble approach to achieve even higher levels of accuracy.

The techniques in this paper represent a considerable step forward in heterogeneous transfer learning by removing the need to rely on instance – instance or feature – feature co-occurrence data.

The purpose of this paper is to develop a model to harness occupancy sensing in a commercial hot-desking environment. Hot-desking is a method of office resource management designed to reduce the real estate costs of professional practices. However, the shortcoming is often in the suitability and appropriateness of allocated work environments. The Internet of Things could produce new data sets in the office at a resolution, speed and validity of which that they could be factored into desk-allocation, distributing seats based on appropriate noise levels, stay length, equipment requirements, previous presence and proximity to others working on the same project, among many others.

The study utilises primary data from a commercial office environment in Central London (numerical building system data and semi-structured interviews) to feed a discrete events simulator. To test the hypothesis, the authors look at the potential for intelligent hot-desking to use “work type” data to improve the distribution of individuals in the office, increasing productivity through the creation of positive “work type environments” – where those working on specific tasks perform better when grouped with others doing the same task. The simulation runs for a typical work day, and the authors compare the intelligent hot-desking arrangement to a base case.

The study shows that sensor data can be used for desk allocation in a hot-desking environment utilising activity-based working, with results that outweigh the costs of occupancy detection. The authors are not only able to optimise desk utilisation based on quality occupancy data but also demonstrate how overall productivity increases as individuals are allocated desks of their preference as much as possible among other enabling optimisations that can be applied. Moreover, the authors explore how an increase in occupancy data collection in the private sector could have key advantages for the business as an organization and the city as a whole.

The research explores only one possible incarnation of intelligent hot-desking, and the authors presume that all data have already been collected, and while not insurmountable, they do not discuss the technical or cultural difficulties to this end. Furthermore, final examination of the productivity benefit – because of the difficulty in defining and measuring the concept – is exploratory rather than definitive. This research suggests that not only human-centric smart building research should be prioritised over energy or space-based themes but also large-scale private sector collection of occupancy data may be imminent, and its potential should be examined.

Findings strongly suggest that the hot-desking may cost more in lost productivity than it gains in reduced rental costs and as such many commercial offices should revaluate the transition, particularly with a view to facilitate intelligent hot-desking. Companies should begin to think strategically about the wider benefits of collecting occupancy data across their real estate portfolio, rather than reviewing use cases in silos. Finally, cities should consider scenarios of widespread collection of occupancy data in the private sector, examining the value these data have to city systems such as transport, and how the city might procure it for these ends.

This paper raises positive and negative social concerns. The value in occupancy data suggested herein, bringing with it the implication it should be collected en mass, has a noted concern that this brings privacy concerns. As such, policy and regulation should heed that current standards should be reviewed to ensure they are sufficient to protect those in offices from being unfairly discriminated, spied or exploited through occupancy data. However, the improved use of occupancy data improving workplaces could indeed make them more enjoyable places to work, and have the potential to become a staple in company’s corporate social responsibility policies.

This paper fulfils an identified need for better understanding the specific uses of occupancy data in the smart building mantra. Several sources suggest the current research focus on energy and rental costs is misguided when the holistic cost of an office is considered, and concepts related to staff – although less understood – may have an order of magnitude bigger impact. This research supports this hypothesis through the example of intelligent hot-desking. The value of this paper lies in redirecting industry and research towards the considering occupancy data in smart building uses cases including – but not limited to– intelligent hot-desking.

Technology proliferation is on a movement to outpace an 18^th-century computing industry paradigm known as “Moore’s law.” This law establishes the rate of technological advancements. The premise of this edict is evident in our coupled workplace with the integration of an emerging technology known as Ambient Intelligence (Aml).

The modernization of the traditional office is designed to be collaborative and environment-friendly. Modernization is primarily due to ambient intelligence. “Opportunities for process and business improvements will derive from a “real-world Web” of smart objects and ambient intelligence, and from consumer-oriented trends such as Web business platforms, aesthetic design, and mobile robots as they move into the business world” (Fenn and Smith, 2005, para. 1). It is safe to reason that ambient intelligence is on a trendy trajectory in many business-oriented workplaces, worksites and workspaces. The business culture is inconspicuously changing before our eyes. Architects and designers are seamlessly incorporating this trend into their respective end-to-end processes of constructing new or retrofitting existing office spaces.

Its unnoticeably embedded adoption is in conference rooms, doorways, elevators, escalators, lighting, meeting rooms, phone displays, and walkways. As ambient technology naturally collides with the functional way an office professional interactively operates through a usual workday, its adaptation becomes seemingly smart and swift. The interesting facet of this technology is that one would not know it unless it was pointed out.

Although there are equipment and devices that offer a singular approach of being convenient and hands-free, there exist common misconceptions and unassuming annoyances that are in place as inherent issues. Once the work environment impedes productivity or natural flow of movement, we realize something is different. These differences align to the surrounding tangible and intangible cues. The information presented in this chapter will disclose the underlying issues at a practical level.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Index by subjects, compiled by K.G.B. Bakewell covering the following journals: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.